agent_panel: plain-language output — simple, short, no jargon/acronyms

Owner request: panel notes and rationale must read like plain everyday
English for a non-trader. Style rule added to the shared take contract
and synthesizer prompt, all few-shot examples rewritten in the target
voice (examples carry the style), and the Telegram votes line now spells
out full stances ("macro: support") instead of "macro:sup".

Before: "BUY against Bollinger SELL and price above upper band; 4h
neutral, mixed MTF"  →  After: "The price is above its usual range, and
the bigger picture is not clearly rising, so buying now is risky."

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SisdfkxnXNL9iZsXxN3T15

CLAUDE.md

@@ -11,8 +11,9 @@ Every numeric threshold in `config.py` and `pair_signals/xauusd.py` was backtest
 ## Commands
 
 ```bash
-python test_all.py                      # QA suite: 359 tests, 22 modules, no network (mocked)
+python test_all.py                      # QA suite: 374 tests, 23 modules, no network (mocked)
-python main.py --pair XAUUSD --timeframe H1 --detail   # CLI signal report (--json for full dump)
+python main.py --pair XAUUSD --timeframe H1 --detail   # CLI signal report (--json for full dump,
+                                        #  --panel to run the advisory LLM agent panel on-demand)
 python telegram_bot.py                  # the bot (refuses to boot without credentials)
 python oanda_daemon.py                  # auto-execution daemon on Oanda practice account
 python dashboard_publisher.py --once    # web dashboard: one scan+publish cycle to $DASHBOARD_OUT_DIR
@@ -34,7 +35,7 @@ Python 3.12; a `venv/` exists in-repo. Dependencies in `requirements.txt` (numpy
 
 ## Configuration & credentials
 
-Precedence for every credential/deployment setting: **env var → config.json → refuse to run** (no hardcoded fallbacks). A gitignored repo-root `.env` is loaded at `app_config` import via `os.environ.setdefault`. `config.json` (gitignored; schema in `config.json.example`) holds secrets plus deployment settings: `data_providers.active` (the live-feed switch), `data_providers.streaming` (optional tick-stream provider for the level-breach monitor), `scanning.pairs`/`timeframes`, per-pair `blocked_hours_utc`, `log_level`. Key env vars: `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`, `TIINGO_API_KEY`, `OANDA_ACCOUNT_ID`/`OANDA_API_TOKEN`/`OANDA_BASE_URL`, `DEEPSEEK_API_KEY` (news LLM; the `deepseek` config.json block holds model/enabled/timeout/daily cap).
+Precedence for every credential/deployment setting: **env var → config.json → refuse to run** (no hardcoded fallbacks). A gitignored repo-root `.env` is loaded at `app_config` import via `os.environ.setdefault`. `config.json` (gitignored; schema in `config.json.example`) holds secrets plus deployment settings: `data_providers.active` (the live-feed switch), `data_providers.streaming` (optional tick-stream provider for the level-breach monitor), `scanning.pairs`/`timeframes`, per-pair `blocked_hours_utc`, `log_level`. Key env vars: `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`, `TIINGO_API_KEY`, `OANDA_ACCOUNT_ID`/`OANDA_API_TOKEN`/`OANDA_BASE_URL`, `DEEPSEEK_API_KEY` (news LLM; the `deepseek` config.json block holds model/enabled/timeout/daily cap). The `agent_panel` config.json block (all-defaults = feature dark) controls the advisory LLM agent panel: `enabled`, per-surface `telegram`/`dashboard` toggles, `min_quality`, `max_panels_per_day`, `reuse_ttl_seconds`.
 
 ## Architecture
 
@@ -59,6 +60,7 @@ Signal currency everywhere is `SignalStrength` (−2..+2). DataFrames are UTC tz
 - **`signal_aggregator.py`** — MTF agreement, diminishing-decay penalty system, exhaustion hard block, quality grading, then an ordered chain of post-grade gates (session, XAU SELL quality/vol/hour, regime blocks, M5 noise gates), then three setup generators: XAU ATR (A+/A), generic swing-based, plus the M5 pullback (`XAU_PB`) and chart-pattern (`XAU_CP`, `chart_patterns.py`) entry paths. The PB/CP generators run AFTER the gate chain and force quality to "B"; the CP path deliberately accepts grade-F composites and re-checks only session/day/big-candle (via warning prefixes) plus its own SELL/vol/RSI gates — it does NOT re-check exhaustion or the M5 min-score/MTF gates. **This is validated behavior, not a bug** (2026-08-04 audit: 226/586 trades in the shipped M5 12mo book are XAU_CP, +38.9R, 104 of them exhaustion-flagged and net-positive) — changing it is a rule-1 revalidation event. `TradeSetup.strategy` selects trailing params; trail params are stamped on the setup from the *producing* config so per-TF overrides reach formatters.
 - **`pair_signals/`** — per-pair config factories. **Always use the factory (`produce_signal` / `make_config`), never bare `PlatformConfig()`** — the factory applies the config.json overlay first, then the validated per-TF deltas (M5 hour-9 block, M15 trail arm 0.70, M15 SELL-hour block, etc.; evidence in the docstring). A test enforces factory ≡ inline-config equivalence. Confirmation map: M5→[M15,H1], M15→[M5,H1], H1→[M15,H4], H4→[H1,D1].
 - **`news_engine.py`** — Forex Factory calendar (MyFXBook RSS fallback) + RSS keyword sentiment with gold-inverse keywords. Class-level caches (fresh engine per scan). Optional DeepSeek enrichment via `llm_client.py` (requests-only, no SDK): batched headline sentiment overriding keyword scores, gold-direction on release surprises, and a market-summary blurb (`SignalReport.news_summary`) — **display/warning-only, keyword fallback on any failure, inert without a key**; prompts are static module constants (DeepSeek prefix-cache pricing) and requests send `thinking: disabled` (V4-Flash reasoning otherwise blows the timeout). By XAU-tuned default, event risk and sentiment **warn but do not gate signals** (`event_risk_caps_signals=False`, `sentiment_affects_signals=False`) — the LLM does not change that.
+- **`agent_panel.py`** — ai-hedge-fund-style advisory panel: 4 DeepSeek persona calls (macro/technical/risk/contrarian, concurrent on a panel-private pool) + 1 synthesizer producing an **ADVISORY-ONLY** consensus (`AGREE/NEUTRAL/OPPOSE` + confidence + rationale) written onto `SignalReport.panel_*` fields. Attached **post-hoc** by consumers (`telegram_bot` alert path, `main --panel`, `dashboard_publisher`) — never runs inside `analyze_from_dataframes` (backtest parity, test-locked) and no gate reads its fields. Static prompts (prefix caching); spend bounded by an exact per-bar cache, a reuse TTL keyed (symbol, TF, direction) so persisting signals don't re-spend every bar, and a `max_panels_per_day` UTC budget (default 20 ≈ ≤100 requests) on top of the shared 500/day client cap. `<2` valid persona takes skips the synthesizer; any failure degrades to no panel, never a changed signal.
 - **`telegram_bot.py`** — owner-locked bot + subscriber channel; candle-boundary auto-scan loops (M5/M15/H1/H4, boundary+10 s), alert quality threshold + fingerprint dedup (committed only after a successful send), 📰 LLM market-context line on alerts when available, WebSocket level-breach monitor fed by the streaming provider (`data_providers.streaming`, falling back to the active provider; auto-disabled when the resolved provider can't stream or has no API key — Oanda does not stream, Tiingo does). Rotating log: `logs/telegram.log`, 3-h rotation × 240 backups.
 - **`oanda_daemon.py` / `oanda_executor.py`** — practice-account auto-execution. Book-parity rules (see daemon docstring): no broker-side TP (trailing model — TP1/TP2 advisory), 5-bar per-TF cooldown (wall-clock approximation, so it expires over weekends where the backtester would still count bars), trail ratchets at bar close once per *observed* bar (a missed/failed cycle skips those bars entirely — the intra-gap high/low never enters the peak). Accepted divergence: one position total; orders send `positionFill=OPEN_ONLY` so a state/broker divergence cancels the order instead of silently netting. State/broker re-sync runs at boot and after any suspicious fill (`_reconcile_positions`).
 - **`dashboard_publisher.py` / `static/dashboard.html`** — web dashboard as a **static publisher** (live LAMP server): standalone daemon scans at candle closes via `produce_signal` and atomically writes `snapshot.json`/`news.json`/`dashboard.html` to `$DASHBOARD_OUT_DIR` (Apache serves them; page is public by owner decision). **Hard independence rule: never imports, and is never imported by, `telegram_bot`/`oanda_daemon`** — confirmation map and boundary math are deliberate local copies, so a web-side failure can only mean a stale page. Due-TF classification uses the RAW wake time (flooring wake−buffer skips every H1/H4 scan — 2026-08-02 bug). News panel reads `NewsEngine.get_fundamental_context()` plus the additive `get_week_events()` accessor (display-only; no signal path calls it); as a separate process it has its own news caches and DeepSeek daily cap. News/calendar rows are clickable (detail dialog); headlines carry `id` = sha1(headline) in news.json, and exactly one on-demand endpoint exists: `static/news_summary.php` (installed by `install_static`), which serves a DeepSeek gold-impact summary per headline — whitelist-gated against news.json ids, cache-forever in `ai/`, own fail-closed daily cap (150/day, file-based, separate from the publisher's), key at `/etc/gold-dashboard/deepseek.key`, never reads repo config. Deployment checklist: `deploy/README-dashboard.md`.

agent_panel.py

@@ -0,0 +1,541 @@
+"""
+ForexBot2 — LLM agent panel (advisory consensus)
+=================================================
+ai-hedge-fund-style persona panel for XAU/USD signals: four DeepSeek
+personas (macro, technical, risk, contrarian) each review a finished
+SignalReport from their own angle, then a synthesizer chair weighs the
+takes and issues a consensus verdict (AGREE / NEUTRAL / OPPOSE with
+confidence and a short rationale).
+
+Two invariants, both load-bearing (CLAUDE.md rules 1 and 4):
+
+- ADVISORY / DISPLAY-ONLY. The verdict is written onto SignalReport
+  fields that no gate and no scoring path ever reads. Any failure —
+  LLM down, cap exhausted, garbage reply — degrades to "no panel",
+  never to a changed or blocked signal.
+- NEVER runs inside ``analyze_from_dataframes``. The backtester replays
+  that function; consumers (telegram_bot, main --panel,
+  dashboard_publisher) attach the panel POST-HOC via ``attach_panel``,
+  so replayed books can never see a panel field. Backtest parity holds
+  by construction.
+
+Cost model: every full panel spends 5 requests (4 personas + 1
+synthesizer) against the shared DeepSeek daily cap (llm_client). Two
+layers bound the spend on top of that cap:
+
+- ``max_panels_per_day`` (config, default 20) — a UTC-day budget of
+  panel *attempts*, mirroring the client's request cap.
+- A reuse TTL (default 3600 s) keyed on (symbol, timeframe, direction):
+  a persisting signal re-serves its cached verdict across bars instead
+  of re-spending. Without this, one sticky M5 dashboard signal would
+  burn 5 calls per 5-minute bar — 1,440/day against the 500 cap.
+
+Prompts are STATIC module constants (DeepSeek prefix-cache pricing —
+see news_engine.py); all variable content travels in the user payload.
+"""
+
+import datetime as _dt
+import logging
+import threading
+import time
+from collections import OrderedDict
+from concurrent.futures import ThreadPoolExecutor, wait as _fwait
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+from app_config import get_app_config
+from llm_client import DEFAULT_TIMEOUT, get_llm_client
+
+if TYPE_CHECKING:  # type hints only — keep the coupling one-way
+    from news_engine import FundamentalContext
+    from signal_aggregator import SignalReport
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MIN_QUALITY = "B"
+DEFAULT_MAX_PANELS_PER_DAY = 20
+DEFAULT_REUSE_TTL_SECONDS = 3600.0
+
+# Quality ranks for the min_quality gate. Deliberately a local copy —
+# telegram_bot's ranking is private and dashboard_publisher must not
+# import telegram_bot (hard independence rule).
+_QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1}
+
+
+# ── DeepSeek prompts ─────────────────────────────────────────────────
+# STATIC constants on purpose: DeepSeek prices a repeated byte-identical
+# prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
+# be interpolated or timestamped. All variable content travels in the
+# user payload. Panel output is display-only — it never gates signals.
+
+_GOLD_DOCTRINE = """\
+Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
+dollar are gold-bearish; easing / rate cuts / safe-haven flows /
+geopolitical stress / weak dollar are gold-bullish.
+"""
+
+_TAKE_CONTRACT = """\
+Your review is advisory research on an already-generated signal; you are
+one voice on a panel, not the decision maker.
+Write the note in plain everyday English for someone who has never
+traded: short simple sentences, no trading jargon, no acronyms, no
+abbreviations. Say "profit versus loss", not "RR"; "the 4-hour chart",
+not "H4"; describe what an indicator MEANS ("the price has climbed
+unusually fast"), never its name or number.
+Reply with ONLY JSON:
+{"stance": "support"|"oppose"|"neutral", "confidence": <0-100>,
+ "note": "<max 25 words>"}
+"stance" says whether YOUR analysis supports the proposed trade
+direction, opposes it, or is genuinely mixed; "confidence" is how sure
+you are of your stance; "note" is your single most important point.
+"""
+
+_MACRO_SYSTEM = """\
+You are the macro analyst on a gold (XAU/USD) trade-review panel. You
+receive JSON: a proposed trade brief ("brief": direction, timeframe,
+quality, price, regime) plus the current fundamental picture — recent
+"headlines", today's high-impact "events" (released: true means the
+number is out), a news "sentiment" score for gold, and an optional
+"news_summary" blurb.
+Judge ONE thing: does the macro backdrop support the proposed direction?
+Weigh released surprises over stale headlines; an imminent unreleased
+high-impact event is a reason for lower confidence, not a direction.
+""" + _GOLD_DOCTRINE + """\
+Example: brief direction BUY, headlines dominated by hawkish Fed
+repricing and a hot CPI print -> {"stance": "oppose", "confidence": 70,
+"note": "Inflation is running hot, so interest rates should stay high — that usually pushes gold down."}
+""" + _TAKE_CONTRACT
+
+_TECH_SYSTEM = """\
+You are the technical analyst on a gold (XAU/USD) trade-review panel.
+You receive JSON: a proposed trade brief ("brief") plus the technical
+evidence — per-"indicators" signal/confidence, multi-timeframe
+"mtf" signals and scores, "key_levels" versus the current price, and an
+"exhaustion" count of consecutive same-direction bars flagged by the
+engine.
+Judge ONE thing: is the technical picture internally coherent for the
+proposed direction? Look for indicators fighting each other, the signal
+fighting the regime (e.g. a BUY in trending_down), higher timeframes
+disagreeing, price pressed into a key level, or an exhausted move being
+chased.
+Example: brief direction SELL in regime trending_up with H1 and H4 both
+BUY -> {"stance": "oppose", "confidence": 80,
+"note": "This bets on gold falling while the bigger picture still points up."}
+""" + _TAKE_CONTRACT
+
+_RISK_SYSTEM = """\
+You are the risk manager on a gold (XAU/USD) trade-review panel. You
+receive JSON: a proposed trade brief ("brief") plus the risk facts —
+the trade "setup" (entry, stop_loss, take_profit_1/2, risk_reward,
+risk_dollars), the "volatility" regime, engine "warnings", the calendar
+"event_risk" level, and the assumed "spread".
+Judge ONE thing: is this setup's risk acceptable as constructed? Check
+reward-to-risk adequacy, stop distance versus the volatility regime
+(a tight stop in extreme vol dies to noise; a huge stop in low vol is
+oversized), spread as a fraction of the stop, and event landmines about
+to hit. A missing setup means the signal graded too low for one —
+oppose unless the rest is exceptional.
+Example: RR 1.4 with volatility extreme and a high-impact event due ->
+{"stance": "oppose", "confidence": 65,
+"note": "The possible profit is small for such wild price swings, and big economic news is due soon."}
+""" + _TAKE_CONTRACT
+
+_CONTRARIAN_SYSTEM = """\
+You are the contrarian on a gold (XAU/USD) trade-review panel — the
+designated skeptic. You receive JSON: a proposed trade brief ("brief"),
+the engine's own "bullish_reasons" and "bearish_reasons", its
+"warnings", and an optional "news_summary".
+Your job: construct the strongest honest case AGAINST the proposed
+trade, then score how damaging that case is. Use "oppose" only when the
+counter-case is genuinely strong, "neutral" when it is real but modest,
+and "support" when even the skeptic finds nothing that bites.
+""" + _GOLD_DOCTRINE + """\
+Example: a BUY whose own engine lists three bearish reasons and a
+counter-trend warning -> {"stance": "oppose", "confidence": 60,
+"note": "The system's own list of negatives looks stronger than its reason to buy."}
+""" + _TAKE_CONTRACT
+
+_SYNTH_SYSTEM = """\
+You are the chair of a gold (XAU/USD) trade-review panel. You receive
+JSON: the proposed trade "direction" and the panel's "takes" — one per
+analyst ("persona", "stance", "confidence", "note").
+Weigh the takes into ONE consensus on whether to act on the signal:
+"AGREE" (the panel backs the trade), "OPPOSE" (the panel is against it),
+or "NEUTRAL" (genuinely split or low conviction). Weigh a high-
+confidence oppose from any single analyst seriously — one strong veto
+can outweigh two lukewarm supports. Do not average blindly.
+Write the rationale in plain everyday English for someone who has never
+traded: short simple sentences, no trading jargon, no acronyms, no
+abbreviations.
+Example rationale: "Three of four analysts are against this: the
+possible profit is too small and the price has already climbed
+unusually fast. Only the news backdrop supports buying."
+Reply with ONLY JSON:
+{"verdict": "AGREE"|"NEUTRAL"|"OPPOSE", "confidence": <0-100>,
+ "rationale": "<max 40 words>"}
+"""
+
+
+# ── Result types ─────────────────────────────────────────────────────
+
+@dataclass
+class PersonaTake:
+    persona: str        # "macro" | "technical" | "risk" | "contrarian"
+    stance: str         # "support" | "oppose" | "neutral"
+    confidence: float   # 0-100
+    note: str
+
+
+@dataclass
+class PanelVerdict:
+    verdict: str        # "AGREE" | "NEUTRAL" | "OPPOSE"
+    confidence: float   # 0-100
+    rationale: str
+    takes: List[PersonaTake] = field(default_factory=list)
+    created_ts: float = 0.0
+
+
+# ── Payload builders (pure functions; llm_client json-dumps sorted) ──
+
+def _direction(report: "SignalReport") -> Optional[str]:
+    """BUY/SELL the panel should review; None = nothing to review."""
+    if report.trade_setup is not None:
+        return report.trade_setup.direction
+    if report.final_signal.value > 0:
+        return "BUY"
+    if report.final_signal.value < 0:
+        return "SELL"
+    return None
+
+
+def _brief(report: "SignalReport") -> Dict[str, Any]:
+    analysis = report.primary_analysis
+    return {
+        "pair": report.display_name,
+        "timeframe": analysis.timeframe if analysis else "",
+        "direction": _direction(report),
+        "quality": report.signal_quality,
+        "score": round(report.final_score, 1),
+        "confidence": round(report.final_confidence),
+        "price": round(report.current_price, 2),
+        "market_regime": report.market_regime,
+        "volatility_regime": report.volatility_regime,
+        "mtf_agreement": report.mtf_agreement,
+    }
+
+
+def _macro_payload(report: "SignalReport",
+                   context: Optional["FundamentalContext"]) -> Dict[str, Any]:
+    headlines: List[str] = []
+    events: List[Dict[str, Any]] = []
+    if context is not None:
+        headlines = [h.headline for h in context.news_headlines[:10]]
+        events = [
+            {"event": e.event, "currency": e.currency,
+             "actual": e.actual, "forecast": e.forecast,
+             "released": not e.is_upcoming}
+            for e in context.events_today if e.is_high_impact
+        ][:8]
+    return {
+        "brief": _brief(report),
+        "headlines": headlines,
+        "events": events,
+        "sentiment": round(report.news_sentiment, 3),
+        "news_summary": report.news_summary,
+    }
+
+
+def _technical_payload(report: "SignalReport") -> Dict[str, Any]:
+    indicators: Dict[str, Dict[str, Any]] = {}
+    key_levels: Dict[str, float] = {}
+    analysis = report.primary_analysis
+    if analysis is not None:
+        # Same visibility filter as main.report_to_dict: private series
+        # ride as _-prefixed pseudo-indicators, weight-0 = context-only.
+        indicators = {
+            name: {"signal": ind.signal.name,
+                   "confidence": round(ind.confidence)}
+            for name, ind in analysis.indicators.items()
+            if not name.startswith("_") and ind.weight != 0
+        }
+        key_levels = {k: round(v, 2) for k, v in analysis.key_levels.items()}
+    return {
+        "brief": _brief(report),
+        "indicators": indicators,
+        "mtf": {tf: {"signal": sig.name,
+                     "score": round(report.mtf_scores.get(tf, 0.0), 1)}
+                for tf, sig in report.mtf_signals.items()},
+        "key_levels": key_levels,
+        "exhaustion": report.exhaustion_count,
+    }
+
+
+def _risk_payload(report: "SignalReport") -> Dict[str, Any]:
+    payload: Dict[str, Any] = {
+        "brief": _brief(report),
+        "volatility": report.volatility_regime,
+        "warnings": list(report.warnings),
+        "event_risk": report.event_risk,
+        "event_description": report.event_description,
+        "spread": round(report.spread_estimate, 2),
+    }
+    ts = report.trade_setup
+    if ts is not None:
+        payload["setup"] = {
+            "direction": ts.direction,
+            "entry": round(ts.entry_price, 2),
+            "stop_loss": round(ts.stop_loss, 2),
+            "take_profit_1": round(ts.take_profit_1, 2),
+            "take_profit_2": round(ts.take_profit_2, 2),
+            "risk_reward": round(ts.risk_reward, 2),
+            "risk_dollars": round(ts.risk_pips * ts.pip_value, 2),
+        }
+    return payload
+
+
+def _contrarian_payload(report: "SignalReport") -> Dict[str, Any]:
+    return {
+        "brief": _brief(report),
+        "bullish_reasons": report.bullish_reasons[:3],
+        "bearish_reasons": report.bearish_reasons[:3],
+        "warnings": list(report.warnings),
+        "news_summary": report.news_summary,
+    }
+
+
+# ── Orchestration ────────────────────────────────────────────────────
+
+# Panel-private pool: the 4 persona calls run concurrently WITHOUT
+# occupying telegram_bot's shared scan executor (which has 4 workers —
+# borrowing them would stall every other scan for the panel's duration).
+_pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix="agent-panel")
+
+_VALID_STANCES = ("support", "oppose", "neutral")
+_VALID_VERDICTS = ("AGREE", "NEUTRAL", "OPPOSE")
+
+
+def _clamp_confidence(raw: Any) -> Optional[float]:
+    try:
+        return max(0.0, min(100.0, float(raw)))
+    except (TypeError, ValueError):
+        return None
+
+
+def _validate_take(persona: str, reply: Any) -> Optional[PersonaTake]:
+    if not isinstance(reply, dict):
+        return None
+    stance = reply.get("stance")
+    if stance not in _VALID_STANCES:
+        return None
+    confidence = _clamp_confidence(reply.get("confidence"))
+    if confidence is None:
+        return None
+    note = str(reply.get("note", "")).strip()[:160]
+    return PersonaTake(persona=persona, stance=stance,
+                       confidence=confidence, note=note)
+
+
+def _ask_persona(persona: str, system: str,
+                 payload: Dict[str, Any]) -> Optional[PersonaTake]:
+    # Client resolved at call time: tests install stubs via lc._client.
+    reply = get_llm_client().chat_json(system, payload)
+    return _validate_take(persona, reply)
+
+
+def run_panel(report: "SignalReport",
+              context: Optional["FundamentalContext"] = None,
+              ) -> Optional[PanelVerdict]:
+    """Run 4 personas + synthesizer; None = no verdict (fail-safe).
+
+    Requires ≥2 valid persona takes before spending the synthesizer
+    request; the synthesizer's reply is validated strictly and there is
+    deliberately NO programmatic fallback verdict — a panel that could
+    not really convene reports nothing rather than something invented.
+    """
+    direction = _direction(report)
+    if direction is None:
+        return None
+    jobs = (
+        ("macro", _MACRO_SYSTEM, _macro_payload(report, context)),
+        ("technical", _TECH_SYSTEM, _technical_payload(report)),
+        ("risk", _RISK_SYSTEM, _risk_payload(report)),
+        ("contrarian", _CONTRARIAN_SYSTEM, _contrarian_payload(report)),
+    )
+    futures = [_pool.submit(_ask_persona, name, system, payload)
+               for name, system, payload in jobs]
+    # The HTTP timeout inside the client bounds each call; the margin
+    # covers pool queueing. Stragglers past the wait are abandoned (their
+    # threads finish harmlessly) and simply don't vote.
+    client_timeout = getattr(get_llm_client(), "_timeout", DEFAULT_TIMEOUT)
+    done, _ = _fwait(futures, timeout=client_timeout + 8.0)
+    takes = [f.result() for f in done if not f.exception()]
+    takes = [t for t in takes if t is not None]
+    if len(takes) < 2:
+        logger.debug("agent panel: %d/4 persona takes — skipping synthesizer",
+                     len(takes))
+        return None
+    order = {"macro": 0, "technical": 1, "risk": 2, "contrarian": 3}
+    takes.sort(key=lambda t: order.get(t.persona, 9))
+    reply = get_llm_client().chat_json(_SYNTH_SYSTEM, {
+        "direction": direction,
+        "takes": [{"persona": t.persona, "stance": t.stance,
+                   "confidence": t.confidence, "note": t.note}
+                  for t in takes],
+    })
+    if not isinstance(reply, dict):
+        return None
+    verdict = reply.get("verdict")
+    confidence = _clamp_confidence(reply.get("confidence"))
+    if verdict not in _VALID_VERDICTS or confidence is None:
+        return None
+    rationale = str(reply.get("rationale", "")).strip()[:240]
+    return PanelVerdict(verdict=verdict, confidence=confidence,
+                        rationale=rationale, takes=takes,
+                        created_ts=time.time())
+
+
+# ── Cache, budget, attach ────────────────────────────────────────────
+
+_state_lock = threading.Lock()
+# Exact result per reviewed bar: repeated attaches on the same signal
+# (re-scan, multi-consumer) are free.
+_cache: "OrderedDict[Tuple[str, str, str, str], PanelVerdict]" = OrderedDict()
+_CACHE_MAX = 32
+# Reuse layer: a persisting signal (same symbol/tf/direction, newer bar)
+# re-serves its last verdict for reuse_ttl_seconds instead of re-spending
+# 5 requests per bar (see module docstring for the M5 dashboard math).
+_last_by_dir: Dict[Tuple[str, str, str], PanelVerdict] = {}
+# UTC-day budget of panel attempts (mirrors llm_client._under_cap).
+_panel_day: Optional[_dt.date] = None
+_panel_count: int = 0
+_warned_budget = False
+
+
+def reset_panel_state() -> None:
+    """Drop caches and counters (tests / config reloads)."""
+    global _panel_day, _panel_count, _warned_budget
+    with _state_lock:
+        _cache.clear()
+        _last_by_dir.clear()
+        _panel_day = None
+        _panel_count = 0
+        _warned_budget = False
+
+
+def panel_health() -> Dict[str, Any]:
+    """Read-only counters for status displays."""
+    settings = get_app_config().agent_panel_settings
+    cap = int(settings.get("max_panels_per_day", DEFAULT_MAX_PANELS_PER_DAY))
+    today = _dt.datetime.now(_dt.timezone.utc).date()
+    with _state_lock:
+        used = _panel_count if _panel_day == today else 0
+    return {"used_today": used, "cap": cap}
+
+
+def _under_panel_budget(cap: int) -> bool:
+    """Count one panel attempt against the UTC-day budget."""
+    global _panel_day, _panel_count, _warned_budget
+    today = _dt.datetime.now(_dt.timezone.utc).date()
+    with _state_lock:
+        if _panel_day != today:
+            _panel_day = today
+            _panel_count = 0
+            _warned_budget = False
+        if _panel_count >= cap:
+            if not _warned_budget:
+                _warned_budget = True
+                logger.warning(
+                    "agent panel: daily budget (%d) exhausted — no more "
+                    "panels until UTC midnight", cap)
+            return False
+        _panel_count += 1
+        return True
+
+
+def _write_fields(report: "SignalReport", v: PanelVerdict) -> None:
+    report.panel_verdict = v.verdict
+    report.panel_confidence = float(v.confidence)
+    report.panel_rationale = v.rationale
+    report.panel_takes = [
+        {"persona": t.persona, "stance": t.stance,
+         "confidence": t.confidence, "note": t.note} for t in v.takes]
+
+
+def attach_panel(report: "SignalReport",
+                 settings: Optional[Dict[str, Any]] = None,
+                 surface: str = "telegram",
+                 force: bool = False,
+                 context: Optional["FundamentalContext"] = None) -> bool:
+    """Best-effort panel attach; True iff panel fields were populated.
+
+    Advisory feature contract: NEVER raises — an exploding panel must
+    not cost an alert send or a dashboard publish. ``force`` (CLI
+    --panel) bypasses the enabled/surface/quality toggles but never the
+    daily budget or the shared LLM cap.
+    """
+    try:
+        if settings is None:
+            settings = get_app_config().agent_panel_settings
+        if not force:
+            if not settings.get("enabled", False):
+                return False
+            surface_on = {
+                "telegram": settings.get("telegram", True),
+                "dashboard": settings.get("dashboard", False),
+            }.get(surface, True)
+            if not surface_on:
+                return False
+            min_quality = settings.get("min_quality", DEFAULT_MIN_QUALITY)
+            if (_QUALITY_RANK.get(report.signal_quality, -1)
+                    < _QUALITY_RANK.get(min_quality, 2)):
+                return False
+        direction = _direction(report)
+        if direction is None:
+            return False
+
+        analysis = report.primary_analysis
+        tf = analysis.timeframe if analysis else ""
+        exact_key = (report.symbol, tf, report.timestamp, direction)
+        dir_key = (report.symbol, tf, direction)
+        ttl = float(settings.get("reuse_ttl_seconds",
+                                 DEFAULT_REUSE_TTL_SECONDS))
+        with _state_lock:
+            hit = _cache.get(exact_key)
+            if hit is None:
+                prior = _last_by_dir.get(dir_key)
+                if prior is not None and time.time() - prior.created_ts < ttl:
+                    hit = prior
+        if hit is not None:
+            _write_fields(report, hit)
+            return True
+
+        cap = int(settings.get("max_panels_per_day",
+                               DEFAULT_MAX_PANELS_PER_DAY))
+        if not _under_panel_budget(cap):
+            return False
+
+        if context is None:
+            # Best-effort; the news engine's class-level 600 s caches make
+            # this nearly free when warm in both bot and dashboard.
+            try:
+                from config import PlatformConfig
+                from news_engine import NewsEngine
+                context = NewsEngine(
+                    PlatformConfig().news).get_fundamental_context()
+            except Exception as e:
+                logger.debug("agent panel: news context unavailable: %s", e)
+                context = None
+
+        verdict = run_panel(report, context)
+        if verdict is None:
+            return False
+        _write_fields(report, verdict)
+        with _state_lock:
+            _cache[exact_key] = verdict
+            while len(_cache) > _CACHE_MAX:
+                _cache.popitem(last=False)
+            _last_by_dir[dir_key] = verdict
+        return True
+    except Exception:
+        logger.exception("agent panel: attach failed — signal unaffected")
+        return False

app_config.py

@@ -86,7 +86,7 @@ _VALID_LOG_LEVELS = ("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL")
 _SCANNABLE_TFS = ("M5", "M15", "H1", "H4")
 
 _KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level",
-              "oanda", "deepseek"}
+              "oanda", "deepseek", "agent_panel"}
 _KNOWN_OANDA = {
     "account_id", "api_token", "base_url", "lot_size",
     "quality_gate", "max_hold_bars",
@@ -94,6 +94,10 @@ _KNOWN_OANDA = {
 _KNOWN_DEEPSEEK = {
     "api_key", "model", "enabled", "timeout_seconds", "daily_request_cap",
 }
+_KNOWN_AGENT_PANEL = {
+    "enabled", "telegram", "dashboard", "min_quality",
+    "max_panels_per_day", "reuse_ttl_seconds",
+}
 _KNOWN_TELEGRAM = {"token", "chat_id", "channel_id", "alert_min_quality"}
 _KNOWN_SCANNING = {"pairs", "timeframes"}
 _KNOWN_PAIR_KEYS = {"blocked_hours_utc"}
@@ -118,6 +122,7 @@ class AppConfig:
     pair_overrides: Dict[str, Dict[str, Any]] = field(default_factory=dict)
     oanda_settings: Dict[str, Any] = field(default_factory=dict)
     deepseek_settings: Dict[str, Any] = field(default_factory=dict)
+    agent_panel_settings: Dict[str, Any] = field(default_factory=dict)
     log_level: str = "INFO"
 
 
@@ -276,6 +281,31 @@ def load_app_config(path: str = CONFIG_PATH) -> AppConfig:
                 "config.json: deepseek.model must be a non-empty string")
     app.deepseek_settings = dict(deepseek)
 
+    panel = raw.get("agent_panel", {})
+    _check_keys(panel, _KNOWN_AGENT_PANEL, '"agent_panel"')
+    for flag in ("enabled", "telegram", "dashboard"):
+        if flag in panel and not isinstance(panel[flag], bool):
+            raise AppConfigError(
+                f"config.json: agent_panel.{flag} must be true or false")
+    if "min_quality" in panel and panel["min_quality"] not in _VALID_QUALITIES:
+        raise AppConfigError(
+            f'config.json: agent_panel.min_quality "{panel["min_quality"]}" '
+            f"not in {_VALID_QUALITIES}")
+    if "max_panels_per_day" in panel:
+        mpd = panel["max_panels_per_day"]
+        if isinstance(mpd, bool) or not isinstance(mpd, int) or mpd < 1:
+            raise AppConfigError(
+                "config.json: agent_panel.max_panels_per_day must be a "
+                "positive integer")
+    if "reuse_ttl_seconds" in panel:
+        ttl = panel["reuse_ttl_seconds"]
+        if isinstance(ttl, bool) or not isinstance(ttl, (int, float)) \
+                or ttl < 0:
+            raise AppConfigError(
+                "config.json: agent_panel.reuse_ttl_seconds must be a "
+                "non-negative number")
+    app.agent_panel_settings = dict(panel)
+
     level = raw.get("log_level", app.log_level)
     if level not in _VALID_LOG_LEVELS:
         raise AppConfigError(

config.json.example

@@ -40,5 +40,13 @@
     "timeout_seconds": 12,
     "daily_request_cap": 500
   },
+  "agent_panel": {
+    "enabled": false,
+    "telegram": true,
+    "dashboard": false,
+    "min_quality": "B",
+    "max_panels_per_day": 20,
+    "reuse_ttl_seconds": 3600
+  },
   "log_level": "INFO"
 }

dashboard_publisher.py

@@ -49,6 +49,7 @@ from datetime import datetime, timezone
 from logging.handlers import TimedRotatingFileHandler
 from typing import Dict, List, Optional
 
+from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, XAUUSD
 from main import report_to_dict
 from news_engine import NewsEngine
@@ -357,6 +358,13 @@ class DashboardPublisher:
         try:
             report = produce_signal(XAUUSD, tf, self._provider(),
                                     CONFIRMATIONS[tf])
+            # Advisory AI panel, post-hoc and display-only. Off unless
+            # config.json agent_panel.dashboard is true. The reuse-TTL
+            # cache + max_panels_per_day budget bound the spend against
+            # this process's own DeepSeek cap (a persisting M5 signal
+            # re-serves its cached verdict instead of re-running every
+            # bar); a fresh panel adds ≤~25 s before this TF publishes.
+            attach_panel(report, surface="dashboard")
             self.snapshots[tf.value] = {
                 "status": "ok",
                 "scanned_at": datetime.now(timezone.utc).isoformat(),

main.py

@@ -20,6 +20,7 @@ if hasattr(sys.stdout, "reconfigure"):
 if hasattr(sys.stderr, "reconfigure"):
     sys.stderr.reconfigure(encoding="utf-8")
 
+from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, SignalStrength, XAUUSD
 from pair_signals import PAIR_PRODUCERS
 from signal_aggregator import SignalAggregator, SignalReport
@@ -163,6 +164,13 @@ def print_signal_report(report: SignalReport, detail: bool = False):
         print(f"  {Colors.MAGENTA}📰{Colors.RESET} {report.news_description}")
     if report.news_summary:
         print(f"  {Colors.MAGENTA}🧠{Colors.RESET} {report.news_summary}")
+    if report.panel_verdict:
+        print(f"  {Colors.CYAN}🤖{Colors.RESET} AI Panel: "
+              f"{Colors.BOLD}{report.panel_verdict}{Colors.RESET} "
+              f"({report.panel_confidence:.0f}%) — {report.panel_rationale}")
+        for t in report.panel_takes:
+            print(f"     {Colors.GRAY}{t['persona']:<11} {t['stance']:<8} "
+                  f"{t['note']}{Colors.RESET}")
 
     # ── 6. DETAIL (only with --detail) ─────────────────────────────
     if detail and report.primary_analysis:
@@ -209,6 +217,10 @@ def report_to_dict(report: SignalReport) -> dict:
         "news_sentiment": round(report.news_sentiment, 3),
         "news_description": report.news_description,
         "news_summary": report.news_summary,
+        "panel_verdict": report.panel_verdict,
+        "panel_confidence": round(report.panel_confidence, 1),
+        "panel_rationale": report.panel_rationale,
+        "panel_takes": report.panel_takes,
         "bullish_reasons": report.bullish_reasons,
         "bearish_reasons": report.bearish_reasons,
         "warnings": report.warnings,
@@ -268,6 +280,9 @@ Examples:
                         help="Primary analysis timeframe (default: H1)")
     parser.add_argument("--detail", action="store_true",
                         help="Show full indicator breakdown")
+    parser.add_argument("--panel", action="store_true",
+                        help="Run the LLM agent panel on the result "
+                             "(advisory only; needs a DeepSeek key)")
     parser.add_argument("--json", action="store_true",
                         help="Output as JSON")
     parser.add_argument("--verbose", "-v", action="store_true",
@@ -323,6 +338,11 @@ def run_live(args, config: PlatformConfig):
     aggregator = SignalAggregator(config)
     report = aggregator.analyze_pair(symbol=config.symbol)
 
+    # Explicit user intent: force bypasses the enabled/surface/quality
+    # toggles, but the daily panel budget and LLM cap still apply.
+    if getattr(args, "panel", False):
+        attach_panel(report, surface="cli", force=True)
+
     if args.json:
         print(json.dumps(report_to_dict(report), indent=2))
         return

signal_aggregator.py

@@ -88,6 +88,15 @@ class SignalReport:
     news_description: str = ""
     news_summary: str = ""  # LLM market-context blurb (display-only)
 
+    # LLM agent-panel consensus (advisory/DISPLAY-ONLY). Populated
+    # post-hoc by consumers via agent_panel.attach_panel — NEVER inside
+    # analyze_from_dataframes (backtest parity) and never read by any
+    # gate or scoring path.
+    panel_verdict: str = ""        # "AGREE" / "NEUTRAL" / "OPPOSE" / ""
+    panel_confidence: float = 0.0
+    panel_rationale: str = ""
+    panel_takes: List[Dict] = field(default_factory=list)
+
     final_signal: SignalStrength = SignalStrength.NEUTRAL
     final_score: float = 0.0
     final_confidence: float = 0.0

static/dashboard.html

@@ -981,6 +981,22 @@ function detailHTML(tf) {
       ${esc(r.news_summary || "")}</div>`);
   }
 
+  /* AI agent panel (advisory) */
+  if (r.panel_verdict) {
+    const vcls = r.panel_verdict === "AGREE" ? "buy"
+      : r.panel_verdict === "OPPOSE" ? "sell" : "neutral";
+    const takes = (r.panel_takes || []).map(t =>
+      `<tr><td class="ind-k">${esc(t.persona)}</td>
+        <td>${esc(t.stance)}</td>
+        <td class="num">${t.confidence != null ? Math.round(t.confidence) : "—"}</td>
+        <td>${esc(t.note || "")}</td></tr>`).join("");
+    out += sec("AI panel (advisory)", `<div class="d-note" style="margin-bottom:8px">
+      <b class="sig-txt ${vcls}">${esc(r.panel_verdict)}</b> ·
+      conf ${Math.round(r.panel_confidence || 0)}%<br>
+      ${esc(r.panel_rationale || "")}</div>
+      ${takes ? `<table class="d-table"><tbody>${takes}</tbody></table>` : ""}`);
+  }
+
   /* provenance */
   out += sec("Scan detail", `<table class="d-table"><tbody>
     <tr><td class="ind-k">Instrument</td><td colspan="5">${esc(r.display_name || r.symbol || "—")}</td></tr>

telegram_bot.py

@@ -71,6 +71,7 @@ from app_config import (
     telegram_channel_id,
 )
 from providers import BaseDataProvider, make_data_provider
+from agent_panel import attach_panel, panel_health
 from llm_client import get_llm_client
 from news_engine import FundamentalContext, NewsEngine
 from pair_signals import PAIR_PRODUCERS
@@ -407,6 +408,17 @@ def format_signal_message(report: SignalReport) -> str:
         extra.append(f"⚠️ {_escape_md(w)}")
     if report.news_summary:
         extra.append(f"📰 {_escape_md(report.news_summary)}")
+    if report.panel_verdict:
+        mark = {"AGREE": "✅", "OPPOSE": "⛔", "NEUTRAL": "➖"}.get(
+            report.panel_verdict, "")
+        extra.append(
+            f"🤖 AI Panel {mark} *{_escape_md(report.panel_verdict)}* "
+            f"({report.panel_confidence:.0f}%) — "
+            f"{_escape_md(report.panel_rationale)}")
+        if report.panel_takes:
+            votes = " · ".join(
+                f"{t['persona']}: {t['stance']}" for t in report.panel_takes)
+            extra.append(f"   _{_escape_md(votes)}_")
     if extra:
         lines += [""] + extra
 
@@ -720,6 +732,13 @@ async def _scan_timeframe(
         async with _alert_lock(key):
             if not _should_alert(key, report):
                 continue
+            # Panel AFTER the quality gate + dedup decision so no LLM
+            # spend on suppressed alerts. Runs on the scan executor to
+            # keep the event loop free; this key's alert lock is held
+            # for the panel's wall time (≤~25 s), which only delays a
+            # same-key duplicate that _should_alert would drop anyway.
+            await asyncio.get_running_loop().run_in_executor(
+                _executor, attach_panel, report)
             detail = format_signal_message(report)
             delivered = False
             for chat_id in _signal_targets():
@@ -855,6 +874,10 @@ async def _handle_breach(bot, pair_symbol: str, price: float, level_name: str) -
         if not _should_alert(key, report):
             return
 
+        # Same post-gate panel attach as the auto-scan path (see
+        # _scan_timeframe for the spend/lock rationale).
+        await asyncio.get_running_loop().run_in_executor(
+            _executor, attach_panel, report)
         header = (
             f"⚡ *LEVEL BREAK* — {_escape_md(display)} crossed "
             f"*{_escape_md(label)}* @ `{_fmt_price(price)}`\n\n"
@@ -1193,6 +1216,10 @@ def _news_health_lines() -> List[str]:
         else:
             lines.append(f"  ✅ DeepSeek: {used}/{cap} calls today,"
                          f" {scored} headlines scored")
+        if get_app_config().agent_panel_settings.get("enabled", False):
+            ph = panel_health()
+            lines.append(f"  🤖 AI panel: {ph['used_today']}/{ph['cap']}"
+                         " panels today")
     return lines
 
 

test_all.py

@@ -6943,6 +6943,388 @@ def _():
         assert "must be > 0" in str(e)
 
 
+print("\n═══ Module 23: LLM agent panel ═══")
+
+
+@test("app_config: agent_panel block parses; absent block → {}")
+def _():
+    app = load_app_config(_write_json({"agent_panel": {
+        "enabled": True, "telegram": True, "dashboard": False,
+        "min_quality": "A", "max_panels_per_day": 10,
+        "reuse_ttl_seconds": 600}}))
+    assert app.agent_panel_settings["enabled"] is True
+    assert app.agent_panel_settings["min_quality"] == "A"
+    assert app.agent_panel_settings["max_panels_per_day"] == 10
+    assert load_app_config(_write_json({})).agent_panel_settings == {}
+    assert AppConfig().agent_panel_settings == {}
+
+
+@test("app_config: agent_panel rejects unknown keys and bad types")
+def _():
+    for payload in (
+        {"agent_panel": {"enabld": True}},               # unknown key
+        {"agent_panel": {"enabled": "yes"}},             # not a bool
+        {"agent_panel": {"telegram": 1}},                # not a bool
+        {"agent_panel": {"dashboard": "no"}},            # not a bool
+        {"agent_panel": {"min_quality": "Z"}},           # invalid grade
+        {"agent_panel": {"max_panels_per_day": 0}},      # not positive
+        {"agent_panel": {"max_panels_per_day": True}},   # bool is not an int
+        {"agent_panel": {"reuse_ttl_seconds": -5}},      # negative
+        {"agent_panel": "oops"},                         # not an object
+    ):
+        try:
+            load_app_config(_write_json(payload))
+            assert False, f"should have raised for {payload}"
+        except AppConfigError:
+            pass
+
+
+import agent_panel as ap
+from signal_aggregator import SignalReport, TradeSetup
+
+
+class _PanelStub:
+    """llm_client-singleton drop-in that routes replies by system prompt."""
+    available = True
+
+    def __init__(self, replies):
+        self.replies = replies      # system prompt -> reply dict | None
+        self.calls = []             # (system_prompt, payload) in call order
+
+    def chat_json(self, system_prompt, user_payload):
+        self.calls.append((system_prompt, user_payload))
+        reply = self.replies.get(system_prompt)
+        return reply(user_payload) if callable(reply) else reply
+
+
+_TAKE_OK = {"stance": "support", "confidence": 70, "note": "fine"}
+_SYNTH_OK = {"verdict": "AGREE", "confidence": 75, "rationale": "panel backs it"}
+_ALL_OK = {ap._MACRO_SYSTEM: _TAKE_OK, ap._TECH_SYSTEM: _TAKE_OK,
+           ap._RISK_SYSTEM: _TAKE_OK, ap._CONTRARIAN_SYSTEM: _TAKE_OK,
+           ap._SYNTH_SYSTEM: _SYNTH_OK}
+_PANEL_ON = {"enabled": True, "telegram": True, "dashboard": False,
+             "min_quality": "B", "max_panels_per_day": 20,
+             "reuse_ttl_seconds": 3600}
+
+
+def _panel_report(quality="A", signal=SignalStrength.BUY):
+    """Real report via the pipeline, then forced directional/quality."""
+    config = PlatformConfig()
+    config.primary_timeframe = Timeframe.H1
+    config.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
+    agg = SignalAggregator(config)
+    dfs = {Timeframe.H1: make_ohlcv(300, seed=42),
+           Timeframe.M15: make_ohlcv(300, seed=43),
+           Timeframe.H4: make_ohlcv(300, seed=44)}
+    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
+    report.final_signal = signal
+    report.signal_quality = quality
+    return report
+
+
+@test("agent_panel: payload builders compact, filtered, JSON-safe")
+def _():
+    report = _panel_report()
+    now = _dt_m19.datetime.now(_dt_m19.timezone.utc)
+    ctx = FundamentalContext(
+        news_headlines=[NewsSentiment(
+            headline=f"h{i}", source="rss", timestamp=now,
+            currencies_mentioned=["USD"], sentiment_score=0.1, keywords=[])
+            for i in range(15)],
+        events_today=[EconomicEvent(
+            time=now, currency="USD", impact="high", event=f"e{i}",
+            forecast="1.0", actual="2.0") for i in range(12)],
+    )
+    macro = ap._macro_payload(report, ctx)
+    assert len(macro["headlines"]) == 10, "headline cap"
+    assert len(macro["events"]) == 8, "event cap"
+    assert macro["events"][0]["released"] is True
+    assert ap._macro_payload(report, None)["headlines"] == []
+
+    tech = ap._technical_payload(report)
+    assert tech["indicators"], "real analysis must yield indicators"
+    hidden = [n for n in tech["indicators"]
+              if n.startswith("_")
+              or report.primary_analysis.indicators[n].weight == 0]
+    assert not hidden, f"private/weight-0 leaked: {hidden}"
+    assert tech["brief"]["direction"] == "BUY"
+    assert tech["brief"]["timeframe"] == report.primary_analysis.timeframe
+
+    assert "setup" not in ap._risk_payload(report), "no setup when None"
+    report.trade_setup = TradeSetup(
+        pair="XAU/USD", display_name="XAU/USD", direction="SELL",
+        entry_price=2000.0, stop_loss=2010.0, take_profit_1=1985.0,
+        take_profit_2=1970.0, risk_reward=1.5, risk_pips=100.0,
+        reward_pips=150.0, pip_value=0.1)
+    risk = ap._risk_payload(report)
+    assert risk["setup"]["risk_reward"] == 1.5
+    assert risk["brief"]["direction"] == "SELL", "setup direction wins"
+
+    contra = ap._contrarian_payload(report)
+    assert len(contra["bullish_reasons"]) <= 3
+
+    for payload in (macro, tech, risk, contra):
+        json.dumps(payload, sort_keys=True, allow_nan=False)
+
+
+@test("agent_panel: persona reply validation clamps and rejects")
+def _():
+    v = ap._validate_take
+    assert v("macro", {"stance": "moon", "confidence": 50, "note": "x"}) is None
+    assert v("macro", {"stance": "support", "confidence": "hot"}) is None
+    assert v("macro", {"stance": "support"}) is None, "missing confidence"
+    assert v("macro", None) is None
+    assert v("macro", "support") is None
+    take = v("risk", {"stance": "oppose", "confidence": 250, "note": "n" * 300})
+    assert take.confidence == 100.0 and len(take.note) == 160
+    assert v("risk", {"stance": "neutral", "confidence": -3, "note": ""}).confidence == 0.0
+
+
+@test("agent_panel: <2 valid takes → None without spending synthesizer")
+def _():
+    try:
+        stub = _PanelStub({ap._MACRO_SYSTEM: _TAKE_OK})   # others → None
+        lc._client = stub
+        assert ap.run_panel(_panel_report()) is None
+        assert ap._SYNTH_SYSTEM not in [s for s, _ in stub.calls], \
+            "synthesizer request must not be spent on a failed panel"
+        assert len(stub.calls) == 4
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: synthesizer failure or garbage verdict → None")
+def _():
+    try:
+        for synth in (None, {"verdict": "MAYBE", "confidence": 50},
+                      {"verdict": "AGREE", "confidence": "hot"}):
+            lc._client = _PanelStub({**_ALL_OK, ap._SYNTH_SYSTEM: synth})
+            assert ap.run_panel(_panel_report()) is None, f"synth={synth}"
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: full run yields verdict + 4 ordered takes")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        stub.replies[ap._SYNTH_SYSTEM] = {
+            "verdict": "OPPOSE", "confidence": 72.4,
+            "rationale": "r" * 300}
+        lc._client = stub
+        v = ap.run_panel(_panel_report())
+        assert v.verdict == "OPPOSE" and v.confidence == 72.4
+        assert len(v.rationale) == 240, "rationale truncated"
+        assert [t.persona for t in v.takes] == \
+            ["macro", "technical", "risk", "contrarian"]
+        synth_payload = dict(stub.calls)[ap._SYNTH_SYSTEM]
+        assert synth_payload["direction"] == "BUY"
+        assert len(synth_payload["takes"]) == 4
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: NEUTRAL signal → no panel even forced")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        lc._client = stub
+        report = _panel_report(signal=SignalStrength.NEUTRAL)
+        assert ap.run_panel(report) is None
+        assert ap.attach_panel(report, _PANEL_ON, force=True,
+                               context=FundamentalContext()) is False
+        assert stub.calls == [], "NEUTRAL must spend nothing"
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: exact + reuse-TTL cache re-attach with zero spend")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        lc._client = stub
+        report = _panel_report()
+        assert ap.attach_panel(report, _PANEL_ON,
+                               context=FundamentalContext()) is True
+        assert report.panel_verdict == "AGREE"
+        assert len(stub.calls) == 5
+
+        # same bar again → exact-key hit
+        report2 = _panel_report()
+        assert ap.attach_panel(report2, _PANEL_ON,
+                               context=FundamentalContext()) is True
+        assert report2.panel_verdict == "AGREE" and len(stub.calls) == 5
+
+        # newer bar, same symbol/tf/direction → reuse-TTL hit
+        report3 = _panel_report()
+        report3.timestamp = "2099-01-01 01:00:00+00:00"
+        assert ap.attach_panel(report3, _PANEL_ON,
+                               context=FundamentalContext()) is True
+        assert len(stub.calls) == 5, "persisting signal must not re-spend"
+
+        # TTL 0 → the reuse layer is off; a new bar re-runs the panel
+        report4 = _panel_report()
+        report4.timestamp = "2099-01-01 02:00:00+00:00"
+        assert ap.attach_panel(report4, {**_PANEL_ON, "reuse_ttl_seconds": 0},
+                               context=FundamentalContext()) is True
+        assert len(stub.calls) == 10
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: daily budget exhausts and re-arms on UTC roll")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        lc._client = stub
+        settings = {**_PANEL_ON, "max_panels_per_day": 1,
+                    "reuse_ttl_seconds": 0}
+        assert ap.attach_panel(_panel_report(), settings,
+                               context=FundamentalContext()) is True
+        assert len(stub.calls) == 5
+        report = _panel_report()
+        report.timestamp = "2099-01-01 01:00:00+00:00"
+        assert ap.attach_panel(report, settings,
+                               context=FundamentalContext()) is False
+        assert len(stub.calls) == 5, "budget-blocked attempt must spend nothing"
+        ap._panel_day = (_dt_m19.datetime.now(_dt_m19.timezone.utc).date()
+                         - _dt_m19.timedelta(days=1))
+        assert ap.attach_panel(report, settings,
+                               context=FundamentalContext()) is True
+        assert len(stub.calls) == 10
+        assert ap.panel_health()["used_today"] == 1
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("agent_panel: enabled/surface/min_quality gates; force bypasses")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        lc._client = stub
+        report = _panel_report()
+        ctx = FundamentalContext()
+        assert ap.attach_panel(report, {}, context=ctx) is False, \
+            "absent config block must keep the feature dark"
+        assert ap.attach_panel(report, {**_PANEL_ON, "enabled": False},
+                               context=ctx) is False
+        assert ap.attach_panel(report, {**_PANEL_ON, "telegram": False},
+                               context=ctx) is False
+        assert ap.attach_panel(report, _PANEL_ON, surface="dashboard",
+                               context=ctx) is False, "dashboard defaults off"
+        assert ap.attach_panel(_panel_report(quality="C"), _PANEL_ON,
+                               context=ctx) is False, "C < min_quality B"
+        assert ap.attach_panel(_panel_report(quality="F"), _PANEL_ON,
+                               context=ctx) is False
+        assert stub.calls == [], "every gate must block before any spend"
+        assert ap.attach_panel(report, {**_PANEL_ON, "dashboard": True},
+                               surface="dashboard", context=ctx) is True
+        ap.reset_panel_state()
+        assert ap.attach_panel(_panel_report(quality="C"),
+                               {**_PANEL_ON, "enabled": False},
+                               force=True, context=ctx) is True, \
+            "force bypasses enabled/surface/quality"
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("panel verdict flows to Telegram message; absent verdict silent")
+def _():
+    import telegram_bot as tb
+    report = _panel_report()
+    assert "AI Panel" not in tb.format_signal_message(report)
+    report.panel_verdict = "OPPOSE"
+    report.panel_confidence = 64.0
+    report.panel_rationale = "counter-trend against aligned H4"
+    report.panel_takes = [
+        {"persona": "macro", "stance": "support", "confidence": 60,
+         "note": "n"},
+        {"persona": "technical", "stance": "oppose", "confidence": 80,
+         "note": "n"},
+    ]
+    msg = tb.format_signal_message(report)
+    assert "AI Panel" in msg and "OPPOSE" in msg and "(64%)" in msg
+    assert "counter-trend against aligned H4" in msg
+    assert "macro: support" in msg and "technical: oppose" in msg
+
+
+@test("backtest parity: analyze_from_dataframes never populates panel")
+def _():
+    try:
+        stub = _PanelStub(dict(_ALL_OK))
+        lc._client = stub
+        config = PlatformConfig()
+        config.primary_timeframe = Timeframe.H1
+        config.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
+        agg = SignalAggregator(config)
+        dfs = {Timeframe.H1: make_ohlcv(300, seed=42),
+               Timeframe.M15: make_ohlcv(300, seed=43),
+               Timeframe.H4: make_ohlcv(300, seed=44)}
+        for use_news in (True, False):
+            report = agg.analyze_from_dataframes(
+                "XAU/USD", dfs, context=FundamentalContext(),
+                use_news=use_news)
+            assert report.panel_verdict == ""
+            assert report.panel_confidence == 0.0
+            assert report.panel_rationale == ""
+            assert report.panel_takes == []
+        assert stub.calls == [], \
+            "the replayable core must never invoke the panel"
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
+@test("report_to_dict carries panel keys → dashboard snapshot propagation")
+def _():
+    from main import report_to_dict as _rtd
+    from dashboard_publisher import report_to_webapp_dict as _rtw
+    report = _panel_report()
+    d = _rtd(report)
+    assert d["panel_verdict"] == "" and d["panel_takes"] == []
+    report.panel_verdict = "AGREE"
+    report.panel_confidence = 77.5
+    report.panel_rationale = "solid confluence"
+    report.panel_takes = [{"persona": "risk", "stance": "support",
+                           "confidence": 66.0, "note": "rr fine"}]
+    wd = _rtw(report)
+    assert wd["panel_verdict"] == "AGREE"
+    assert wd["panel_confidence"] == 77.5
+    assert wd["panel_takes"][0]["persona"] == "risk"
+    json.dumps(wd, allow_nan=False)
+
+
+@test("agent_panel: attach never raises (LLM explosion, garbage settings)")
+def _():
+    class _Bomb:
+        available = True
+
+        def chat_json(self, system_prompt, user_payload):
+            raise RuntimeError("boom")
+    try:
+        lc._client = _Bomb()
+        report = _panel_report()
+        assert ap.attach_panel(report, _PANEL_ON,
+                               context=FundamentalContext()) is False
+        assert report.panel_verdict == "" and report.panel_takes == []
+        logging.disable(logging.CRITICAL)   # silence the expected traceback
+        try:
+            assert ap.attach_panel(report, 42,
+                                   context=FundamentalContext()) is False
+        finally:
+            logging.disable(logging.NOTSET)
+    finally:
+        lc._client = None
+        ap.reset_panel_state()
+
+
 # ══════════════════════════════════════════════════════════════════════
 #  SUMMARY
 # ══════════════════════════════════════════════════════════════════════